Online ISSN: 2515-8260

Keywords : ECG


Correlation of peak amplitude ECG between leads Based on the condition of the heart

Sabar Setiawidayat

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 2, Pages 862-872

Non-invasive cardiac examination in standard clinic is still using 12-lead electrocardiograph. The results of the examination are presented on ECG paper or on the monitor screen. A normal electrocardiogram on one lead is not necessarily normal for the other lead, because each lead represents a certain part of the heart so that one by one is necessary. This examination takes time so that it can increase the stage of the disease if the patient turns out to be in an abnormal condition. This paper aims to correlate the peak amplitude of each lead to normal and abnormal heart conditions. If it is known that the peak amplitude is correlated between the leads, the other leads do not need to be checked, so that the diagnosis time will be obtained faster. Cardiac biosignal data that has been sampled with a frequency of 250 Hz is a discrete signal that can be stored digitally in a database. 10 samples of normal conditions and 10 samples of abnormal conditions were analyzed using Saphiro-Wilk so that the data were normally distributed. Spearman correlation analysis is used to get peak amplitude correlation between leads. The results showed that for abnormal conditions with a significance of 0.01 there was a correlation between the peak P lead I with leads III and V5, while for normal conditions there was a correlation between peak P lead I and leads V3 and V4. In abnormal conditions there is a correlation between peak R lead II and V6, while in normal conditions there is a correlation between peak R lead I and aVF.

Association Of Spot Urinary Albumin Creatinine Ratio (UACR) With Coronary Artery Disease

Sanyukta H; A.H. Inamdar; Sunil Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 1962-1966

Background: Microalbuminuria has been recently found to be a marker of
atherogenesis. It is associated with various risk factors of atherogenesis and damage of the
vascular endothelium. There have been few studies in which it has been found that
mortality in cardiovascular disease are increased with microalbuminuria. Urine is
collected over 24 hours and this study has similar sensitivity as urinary albumin creatinine
ratio. A urinary albumin to creatinine ratio is as sensitive as a 24 hours urine study even
though it itself is a gold standard test. In patients with CAD other factors have been
explored then urinary albumin has been recently identified.
Objectives: To assess correlation between urinary albumin creatinine ratio with coronary
artery disease and to correlate Urinary albumin creatinine ratio (UACR) with
Framingham heart study Cardiovascular disease (FHS-CVD) score in all patients of
coronary artery disease.
Methodology: In this study which is cross sectional, Subjects to be included in the study
will be explained regarding the study and proper consent will be obtained. A random urine
sample will be collected. Urinary albumin concentration will be measured by auto analyser
and urine albumin creatinine ratio will be calculated. The values of the same will be
recorded and will be statistically analysed.

Noise Removal in ECG Signal Using Digital Filters

N. Sasirekha; P. Vivek Karthick; T. Premakumari; J. Harirajkumar; S. Aishwarya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5145-5149

The Electro Cardio Gram (ECG) is a parametric index to diagnose heart diseases. During the process of acquisition of the ECG signals, it is added up by large amount of noise, which affects the patient diagnosis with respect to telemedicine. The noisy ECG signals have drift in baseline, motion electrodes artefacts, interference of line, muscle contraction noise, etc. Noise reduction is accomplished by making using of adaptive filter which employs wavelet transform. Computer simulation results are shown for the improvement in performance. This methodology adapted successfully removes various types of noise with Signal to noise ratio (SNR). The impact of noise and removal of it are shown in the waveforms and the methodology adopted has produced 82% improvement on the SNR of de-noised signals.